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2020 – today
- 2024
[c12]Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R. Maiya, Max Ehrlich, Abhinav Shrivastava:
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing Their Contributions. CVPR 2024: 10957-10967- [c11]Soumik Mukhopadhyay
, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Jun Ohya, Abhinav Shrivastava:
Do Text-Free Diffusion Models Learn Discriminative Visual Representations? ECCV (60) 2024: 253-272 - [c10]Shishira R. Maiya, Anubhav Gupta, Matthew Gwilliam, Max Ehrlich, Abhinav Shrivastava:
Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics. ECCV (15) 2024: 285-302 - [c9]Archit Ramanasai Kambhamettu, Abhinav Shrivastava, Matthew Gwilliam:
Quantifying NBA Shot Quality: A Deep Network Approach. MMSports@MM 2024: 91-95 - [c8]Connor Anderson, Matthew Gwilliam, Evelyn Gaskin, Ryan Farrell:
Elusive Images: Beyond Coarse Analysis for Fine-Grained Recognition. WACV 2024: 818-828 - [i13]Namitha Padmanabhan, Matthew Gwilliam, Pulkit Kumar, Shishira R. Maiya, Max Ehrlich, Abhinav Shrivastava:
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions. CoRR abs/2401.10217 (2024) - [i12]Shishira R. Maiya, Anubhav Gupta, Matthew Gwilliam, Max Ehrlich, Abhinav Shrivastava:
Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics. CoRR abs/2408.02672 (2024) - [i11]Eric Zhu, Mara Levy, Matthew Gwilliam, Abhinav Shrivastava:
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields. CoRR abs/2411.02482 (2024) - 2023
- [c7]Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava:
HNeRV: A Hybrid Neural Representation for Videos. CVPR 2023: 10270-10279 - [i10]Hao Chen, Matthew Gwilliam, Ser-Nam Lim, Abhinav Shrivastava:
HNeRV: A Hybrid Neural Representation for Videos. CoRR abs/2304.02633 (2023) - [i9]Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Srinidhi Hegde, Tianyi Zhou, Abhinav Shrivastava:
Diffusion Models Beat GANs on Image Classification. CoRR abs/2307.08702 (2023) - [i8]Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava:
Do text-free diffusion models learn discriminative visual representations? CoRR abs/2311.17921 (2023) - [i7]Matthew Gwilliam, Michael Cogswell, Meng Ye, Karan Sikka, Abhinav Shrivastava, Ajay Divakaran:
A Video is Worth 10, 000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval. CoRR abs/2312.00115 (2023) - 2022
- [c6]Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava:
CNeRV: Content-adaptive Neural Representation for Visual Data. BMVC 2022: 510 - [c5]Matthew Gwilliam, Abhinav Shrivastava:
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. CVPR 2022: 9632-9642 - [i6]Matthew Gwilliam, Abhinav Shrivastava:
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning. CoRR abs/2206.08347 (2022) - [i5]Hao Chen, Matthew Gwilliam, Bo He, Ser-Nam Lim, Abhinav Shrivastava:
CNeRV: Content-adaptive Neural Representation for Visual Data. CoRR abs/2211.10421 (2022) - 2021
- [c4]Eva Vanmassenhove, Dimitar Sht. Shterionov, Matthew Gwilliam:
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. EACL 2021: 2203-2213 - [c3]Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu, Alex Hanson:
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. ICCVW 2021: 4106-4115 - [c2]Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell:
Fair Comparison: Quantifying Variance in Results for Fine-grained Visual Categorization. WACV 2021: 3308-3317 - [i4]Eva Vanmassenhove, Dimitar Sht. Shterionov, Matthew Gwilliam:
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation. CoRR abs/2102.00287 (2021) - [i3]Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell:
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization. CoRR abs/2109.03156 (2021) - [i2]Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu, Alex Hanson:
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets. CoRR abs/2109.03229 (2021) - 2020
- [c1]Matthew Gwilliam, Ryan Farrell:
Intelligent Image Collection: Building the Optimal Dataset. WACV 2020: 785-794 - [i1]Connor Anderson, Matthew Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell:
Facing the Hard Problems in FGVC. CoRR abs/2006.13190 (2020)
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